Investigation of household private car ownership considering interdependent consumer preference.
People are connected by various social networks, resulting in the interdependence of consumer choice. Therefore, it is very important and realistic to assume choice interdependence in private car ownership modeling. In this paper, we investigate the interdependence of private car ownership choice us...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2019-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0219212 |
| _version_ | 1830447985767481344 |
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| author | Na Wu Chunyan Tang |
| author_facet | Na Wu Chunyan Tang |
| author_sort | Na Wu |
| collection | DOAJ |
| description | People are connected by various social networks, resulting in the interdependence of consumer choice. Therefore, it is very important and realistic to assume choice interdependence in private car ownership modeling. In this paper, we investigate the interdependence of private car ownership choice using a spatial autoregressive binary probit model estimated by the Bayesian Markov chain Monte Carlo (MCMC) method. Constructing the autoregressive matrix demographically shows that the private car ownership choice of a household is dependent on other household choices. Compared with the pure binary probit model estimated by the MCMC method, the spatial autoregressive model achieves a significant improvement both in loglikelihood value and log marginal density value, which are calculated using the importance sampling method of Newton and Raftery, from approximately -202 to approximately -63 and from -208 to -145, respectively. Moreover, the results indicated by the spatial autoregressive probit model suggest that the number of children, the ownership of an apartment or the availability of a parking lot are positively and significantly associated with the private car ownership level. To test the out-of-sample performance of the model, we estimate the model using 600 data points and test it using another 148 data points. The results indicate that the predictive power is greatly improved. Finally, we analyze the augmented parameter and discover that it is associated with the parking variable in addition to the license variable. |
| first_indexed | 2024-12-21T07:31:58Z |
| format | Article |
| id | doaj.art-45ad923ea26e454294893bad8038266e |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T07:31:58Z |
| publishDate | 2019-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-45ad923ea26e454294893bad8038266e2022-12-21T19:11:32ZengPublic Library of Science (PLoS)PLoS ONE1932-62032019-01-01147e021921210.1371/journal.pone.0219212Investigation of household private car ownership considering interdependent consumer preference.Na WuChunyan TangPeople are connected by various social networks, resulting in the interdependence of consumer choice. Therefore, it is very important and realistic to assume choice interdependence in private car ownership modeling. In this paper, we investigate the interdependence of private car ownership choice using a spatial autoregressive binary probit model estimated by the Bayesian Markov chain Monte Carlo (MCMC) method. Constructing the autoregressive matrix demographically shows that the private car ownership choice of a household is dependent on other household choices. Compared with the pure binary probit model estimated by the MCMC method, the spatial autoregressive model achieves a significant improvement both in loglikelihood value and log marginal density value, which are calculated using the importance sampling method of Newton and Raftery, from approximately -202 to approximately -63 and from -208 to -145, respectively. Moreover, the results indicated by the spatial autoregressive probit model suggest that the number of children, the ownership of an apartment or the availability of a parking lot are positively and significantly associated with the private car ownership level. To test the out-of-sample performance of the model, we estimate the model using 600 data points and test it using another 148 data points. The results indicate that the predictive power is greatly improved. Finally, we analyze the augmented parameter and discover that it is associated with the parking variable in addition to the license variable.https://doi.org/10.1371/journal.pone.0219212 |
| spellingShingle | Na Wu Chunyan Tang Investigation of household private car ownership considering interdependent consumer preference. PLoS ONE |
| title | Investigation of household private car ownership considering interdependent consumer preference. |
| title_full | Investigation of household private car ownership considering interdependent consumer preference. |
| title_fullStr | Investigation of household private car ownership considering interdependent consumer preference. |
| title_full_unstemmed | Investigation of household private car ownership considering interdependent consumer preference. |
| title_short | Investigation of household private car ownership considering interdependent consumer preference. |
| title_sort | investigation of household private car ownership considering interdependent consumer preference |
| url | https://doi.org/10.1371/journal.pone.0219212 |
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